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import streamlit as st
from hugchat import hugchat
from hugchat.login import Login
from metaphor_python import Metaphor
# App title
st.set_page_config(page_title="HugChat with Metaphor")
# Define Metaphor API key
METAPHOR_API_KEY = "1cd6d71b-e530-4ea3-bb18-e9599e641f66" # Replace with your Metaphor API key
with st.sidebar:
st.title('🤗💬 HugChat x Metaphor')
if ('EMAIL' in st.secrets) and ('PASS' in st.secrets):
st.success('HuggingFace Login credentials already provided!', icon='✅')
hf_email = st.secrets['EMAIL']
hf_pass = st.secrets['PASS']
else:
hf_email = st.text_input('Enter E-mail:', type='password')
hf_pass = st.text_input('Enter password:', type='password')
if not (hf_email and hf_pass):
st.warning('Please enter your credentials!', icon='⚠️')
else:
st.success('Proceed to entering your prompt message!', icon='👉')
# Create Metaphor client
metaphor = Metaphor(METAPHOR_API_KEY)
# Store LLM generated responses
if "messages" not in st.session_state:
st.session_state.messages = [{"role": "assistant", "content": "Heya Metaphor bot this side, how may i assist ?"}]
# Display or clear chat messages
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.write(message["content"])
def clear_chat_history():
st.session_state.messages = [{"role": "assistant", "content": "Heya Metaphor bot this side, how may i assist?"}]
st.sidebar.button('Clear Chat History', on_click=clear_chat_history)
# Function for generating LLM response
def generate_response(prompt_input, email, passwd):
# Hugging Face Login
sign = Login(email, passwd)
cookies = sign.login()
# Create ChatBot
chatbot = hugchat.ChatBot(cookies=cookies.get_dict())
# Check if the user's input is a specific question
if prompt_input.strip().lower() in ["who are you?", "who made you?"]:
response = "I am an AI LLama Hugchat of Huggingface which is integrated with Metaphor in the backend."
else:
# Fetch Metaphor search results
search_options = {
"query": prompt_input,
"num_results": 5 # You can adjust the number of results as needed
}
try:
search_response = metaphor.search(**search_options)
# Extract links and summaries from the Metaphor search results
links_and_summaries = [
f"Title: {result.title}\nURL: {result.url}\nSummary: {result.extract}\n---"
for result in search_response.results
]
# Combine the user's query and Metaphor output with the previous conversation
string_dialogue = "You are a helpful assistant."
for dict_message in st.session_state.messages:
if dict_message["role"] == "user":
string_dialogue += "User: " + dict_message["content"] + "\n\n"
else:
string_dialogue += "Assistant: " + dict_message["content"] + "\n\n"
prompt = f"{string_dialogue}\n{prompt_input}\n{''.join(links_and_summaries)}\n Assistant: "
response = chatbot.chat(prompt)
except Exception as e:
response = str(e)
return response
# User-provided prompt
if prompt := st.chat_input(disabled=not (hf_email and hf_pass)):
st.session_state.messages.append({"role": "user", "content": prompt})
with st.chat_message("user"):
st.write(prompt)
# Generate a new response if the last message is not from the assistant
if st.session_state.messages[-1]["role"] != "assistant":
with st.chat_message("assistant"):
with st.spinner("Thinking..."):
response = generate_response(prompt, hf_email, hf_pass)
st.write(response)
message = {"role": "assistant", "content": response}
st.session_state.messages.append(message) |